{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "\n",
    "#importing dataset month wise\n",
    "df_jan=pd.read_csv('january.csv',low_memory=False)\n",
    "df_feb=pd.read_csv('february.csv',low_memory=False)\n",
    "df_mar=pd.read_csv('march.csv',low_memory=False)\n",
    "df_apr=pd.read_csv('april.csv',low_memory=False)\n",
    "df_may=pd.read_csv('may.csv',low_memory=False)\n",
    "df_jun=pd.read_csv('june.csv',low_memory=False)\n",
    "df_jul=pd.read_csv('july.csv',low_memory=False)\n",
    "df_aug=pd.read_csv('august.csv',low_memory=False)\n",
    "df_sep=pd.read_csv('september.csv',low_memory=False)\n",
    "df_oct=pd.read_csv('october.csv',low_memory=False)\n",
    "df_nov=pd.read_csv('november.csv',low_memory=False)\n",
    "df_dec=pd.read_csv('december.csv',low_memory=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AA0101', 'AA0102', 'AA0103', 'AA0104', 'AA0105', 'AA0106',\n",
       "       'AA0107', 'AA0108', 'AA0109', 'AA0110', 'AA0111', 'AA0112',\n",
       "       'AA0115', 'AA0116', 'AA0201', 'AA0202', 'AA0203', 'AA0204',\n",
       "       'AA0205', 'AA0206', 'AA0209', 'AA0210', 'AA0213', 'AA0214',\n",
       "       'AA0215', 'AA0216', 'AA0113'], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jan['vru+line'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cleansing data for better use\n",
    "# making  it to an appropriate type;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_jan['customer_id']=df_jan['customer_id'].astype(int)\n",
    "df_feb['customer_id']=df_feb['customer_id'].astype(int)\n",
    "df_mar['customer_id']=df_mar['customer_id'].astype(int)\n",
    "df_apr['customer_id']=df_apr['customer_id'].astype(int)\n",
    "df_may['customer_id']=df_may['customer_id'].astype(int)\n",
    "df_jun['customer_id']=df_jun['customer_id'].astype(int)\n",
    "df_jul['customer_id']=df_jul['customer_id'].astype(int)\n",
    "df_aug['customer_id']=df_aug['customer_id'].astype(int)\n",
    "df_sep['customer_id']=df_sep['customer_id'].astype(int)\n",
    "df_oct['customer_id']=df_oct['customer_id'].astype(int)\n",
    "df_nov['customer_id']=df_nov['customer_id'].astype(int)\n",
    "df_dec['customer_id']=df_dec['customer_id'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jan['priority'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PS', 'NW', 'PE', 'TT', 'IN', 'NE', 'AA'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jan['type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AGENT      27060\n",
       "HANG        4299\n",
       "PHANTOM      240\n",
       "Name: outcome, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#COUNT THE VALUE OF EACH SERVICE PROVIDED BY CALL CENTRE\n",
    "\n",
    "df_jan['outcome'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#REMOVING THE CUSTOMER WHO DONT HAVE CUSTOMER ID\n",
    "# df_jan['customer_id']=df_jan['customer_id'].abs()\n",
    "# df_jan.sort_values('customer_id')\n",
    "df_jan['customer_id']=df_jan['customer_id'].abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# droping record having cusotmer value marked 0\n",
    "df_jan=df_jan[df_jan['customer_id']!=0]\n",
    "df_feb=df_feb[df_feb['customer_id']!=0]\n",
    "df_mar=df_mar[df_mar['customer_id']!=0]\n",
    "df_apr=df_apr[df_apr['customer_id']!=0]\n",
    "df_may=df_may[df_may['customer_id']!=0]\n",
    "df_jun=df_jun[df_jun['customer_id']!=0]\n",
    "df_jul=df_jul[df_jul['customer_id']!=0]\n",
    "df_aug=df_aug[df_aug['customer_id']!=0]\n",
    "df_sep=df_sep[df_sep['customer_id']!=0]\n",
    "df_oct=df_oct[df_oct['customer_id']!=0]\n",
    "df_nov=df_nov[df_nov['customer_id']!=0]\n",
    "df_dec=df_dec[df_dec['customer_id']!=0]\n",
    "\n",
    "#converting to float datatype for computation\n",
    "df_jul['ser_time']=df_jul['ser_time'].astype('float')\n",
    "df_aug['ser_time']=df_aug['ser_time'].astype('float')\n",
    "df_sep['ser_time']=df_sep['ser_time'].astype('float')\n",
    "df_oct['ser_time']=df_oct['ser_time'].astype('float')\n",
    "\n",
    "df_jul['vru_time']=df_jul['vru_time'].astype('float')\n",
    "df_jul['q_time']=df_jul['q_time'].astype('float')\n",
    "df_aug['vru_time']=df_aug['vru_time'].astype('float')\n",
    "df_aug['q_time']=df_aug['q_time'].astype('float')\n",
    "df_sep['vru_time']=df_sep['vru_time'].astype('float')\n",
    "df_sep['q_time']=df_sep['q_time'].astype('float')\n",
    "df_oct['vru_time']=df_oct['vru_time'].astype('float')\n",
    "df_oct['q_time']=df_oct['vru_time'].astype('float')\n",
    "df_dec['vru_time']=df_dec['vru_time'].astype('float')\n",
    "df_dec['q_time']=df_dec['q_time'].astype('float')\n",
    "\n",
    "# Removing agent with NO_SERVER\n",
    "agent_jan=df_jan[(df_jan['server']!='NO_SERVER')]\n",
    "agent_feb=df_feb[(df_feb['server']!='NO_SERVER')]\n",
    "agent_mar=df_mar[(df_mar['server']!='NO_SERVER')]\n",
    "agent_apr=df_apr[(df_apr['server']!='NO_SERVER')]\n",
    "agent_may=df_may[(df_may['server']!='NO_SERVER')]\n",
    "agent_jun=df_jun[(df_jun['server']!='NO_SERVER')]\n",
    "agent_jul=df_jul[(df_jul['server']!='NO_SERVER')]\n",
    "agent_aug=df_aug[(df_aug['server']!='NO_SERVER')]\n",
    "agent_sep=df_sep[(df_sep['server']!='NO_SERVER')]\n",
    "agent_oct=df_oct[(df_oct['server']!='NO_SERVER')]\n",
    "agent_nov=df_nov[(df_nov['server']!='NO_SERVER')]\n",
    "agent_dec=df_dec[(df_dec['server']!='NO_SERVER')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calulating volume of data each month \n",
    "# which month has higher volume of calls data\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#incresing the size of the frame to display pie chart\n",
    "# print(plt.rcParams.get('figure.figsize'))\n",
    "\n",
    "\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 10\n",
    "fig_size[1] = 8\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size\n",
    "\n",
    "\n",
    "\n",
    "volume_of_data_per_month=[len(df_jan.index),len(df_feb.index),len(df_mar.index),len(df_apr.index),\n",
    "                         len(df_may.index),len(df_jun.index),len(df_jul.index),len(df_aug.index),\n",
    "                         len(df_sep.index),len(df_oct.index),len(df_nov.index),len(df_dec.index)]\n",
    "\n",
    "month=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']\n",
    "#pie chart\n",
    "\n",
    "\n",
    "plt.pie(volume_of_data_per_month,labels =month , autopct='%1.2f%%',\n",
    "        startangle =60, shadow = True,explode = (0.1, 0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0))\n",
    "plt.axis('equal')\n",
    "plt.suptitle('Volume of data Generated each month',fontsize=30)\n",
    "plt.xlabel(\"Data Generated\",fontsize=20)\n",
    "plt.show()\n",
    "\n",
    "#bar graph for calls \n",
    "plt.bar(month,volume_of_data_per_month)\n",
    "plt.suptitle(\"Each month calls Data\",fontsize=30)\n",
    "plt.xlabel(\"Calls per month\",fontsize=20)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calculating average call duration.\n",
    "#calculate call duration each month.\n",
    "#first call resolution.\n",
    "\n",
    "\n",
    "#calculating average call duration served by the agent\n",
    "avg_call_duration=[df_jan['ser_time'].mean(),\n",
    "                   df_feb['ser_time'].mean(),\n",
    "                   df_mar['ser_time'].mean(),\n",
    "                   df_apr['ser_time'].mean(),\n",
    "                   df_may['ser_time'].mean(),\n",
    "                   df_jun['ser_time'].mean(),\n",
    "                   df_jul['ser_time'].mean(),\n",
    "                   df_aug['ser_time'].mean(),\n",
    "                   df_sep['ser_time'].mean(),\n",
    "                   df_oct['ser_time'].mean(),\n",
    "                   df_nov['ser_time'].mean(),\n",
    "                   df_dec['ser_time'].mean()]\n",
    "\n",
    "call_duration_pmonth=[df_jan['ser_time'].sum()/3600,\n",
    "                      df_feb['ser_time'].sum()/3600,\n",
    "                      df_mar['ser_time'].sum()/3600,\n",
    "                      df_apr['ser_time'].sum()/3600,\n",
    "                      df_may['ser_time'].sum()/3600,\n",
    "                      df_jun['ser_time'].sum()/3600,\n",
    "                      df_jul['ser_time'].sum()/3600,\n",
    "                      df_aug['ser_time'].sum()/3600,\n",
    "                      df_sep['ser_time'].sum()/3600,\n",
    "                      df_oct['ser_time'].sum()/3600,\n",
    "                      df_nov['ser_time'].sum()/3600,\n",
    "                      df_dec['ser_time'].sum()/3600]\n",
    "\n",
    "#plot average vs normal call duration\n",
    "df = pd.DataFrame({'Call Duration': call_duration_pmonth,\n",
    "                   'Average call duration':avg_call_duration}, index=month)\n",
    "ax = df.plot.barh()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first call resolution\n",
    "\n",
    "first_call_resolve_jan=df_jan[(df_jan['ser_time']<30) & (df_jan['server']!='NO_SERVER')]\n",
    "first_call_resolve_feb=df_feb[(df_feb['ser_time']<30) & (df_feb['server']!='NO_SERVER')]\n",
    "first_call_resolve_mar=df_mar[(df_mar['ser_time']<30) & (df_mar['server']!='NO_SERVER')]\n",
    "first_call_resolve_apr=df_apr[(df_apr['ser_time']<30) & (df_apr['server']!='NO_SERVER')]\n",
    "first_call_resolve_may=df_may[(df_may['ser_time']<30) & (df_may['server']!='NO_SERVER')]\n",
    "first_call_resolve_jun=df_jun[(df_jun['ser_time']<30) & (df_jun['server']!='NO_SERVER')]\n",
    "first_call_resolve_jul=df_jul[(df_jul['ser_time']<30) & (df_jul['server']!='NO_SERVER')]\n",
    "first_call_resolve_aug=df_aug[(df_aug['ser_time']<30) & (df_aug['server']!='NO_SERVER')]\n",
    "first_call_resolve_sep=df_sep[(df_sep['ser_time']<30) & (df_sep['server']!='NO_SERVER')]\n",
    "first_call_resolve_oct=df_oct[(df_oct['ser_time']<30) & (df_oct['server']!='NO_SERVER')]\n",
    "first_call_resolve_nov=df_nov[(df_nov['ser_time']<30) & (df_nov['server']!='NO_SERVER')]\n",
    "first_call_resolve_dec=df_dec[(df_dec['ser_time']<30) & (df_dec['server']!='NO_SERVER')]\n",
    "\n",
    "issue_resolve_at_first_call=[len(first_call_resolve_jan)*100/len(df_jan),len(first_call_resolve_feb)*100/len(df_feb),len(first_call_resolve_mar)*100/len(df_mar),len(first_call_resolve_apr)*100/len(df_apr),\n",
    "                            len(first_call_resolve_may)*100/len(df_may),len(first_call_resolve_jun)*100/len(df_jun),len(first_call_resolve_jul)*100/len(df_jul),len(first_call_resolve_aug)*100/len(df_aug),\n",
    "                            len(first_call_resolve_sep)*100/len(df_sep),len(first_call_resolve_oct)*100/len(df_oct),len(first_call_resolve_nov)*100/len(df_nov),len(first_call_resolve_dec)*100/len(df_dec)]\n",
    "\n",
    "\n",
    "#print(len(first_call_resolve_jan)/len(df_jan))\n",
    "\n",
    "plt.title(\"% First Call Response Given on Each Month\",fontsize=15)\n",
    "plt.barh(month,issue_resolve_at_first_call,)\n",
    "plt.xlabel('In Percentage')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#pie Chart\n",
    "plt.pie(issue_resolve_at_first_call,labels=month,autopct='%.2f%%',\n",
    "        shadow=True,explode=(0, 0, 0, 0, 0.5, 0, 0, 0, 0.5,0,0, 0.5))\n",
    "plt.suptitle('First Call Response by Agent Each Month (in Percentage)',fontsize=15)\n",
    "plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#average response time to service a customer\n",
    "#most frequent customer per month\n",
    "\n",
    "# first_call_resolve_jan=df_jan[(df_jan['ser_time']<30) & (df_jan['server']!='NO_SERVER')]\n",
    "avg_response_time_jan=(first_call_resolve_jan['vru_time']+first_call_resolve_jan['q_time']).sum()/len(first_call_resolve_jan)\n",
    "avg_response_time_feb=(first_call_resolve_feb['vru_time']+first_call_resolve_feb['q_time']).sum()/len(first_call_resolve_feb)\n",
    "avg_response_time_mar=(first_call_resolve_mar['vru_time']+first_call_resolve_mar['q_time']).sum()/len(first_call_resolve_mar)\n",
    "avg_response_time_apr=(first_call_resolve_apr['vru_time']+first_call_resolve_apr['q_time']).sum()/len(first_call_resolve_apr)\n",
    "avg_response_time_may=(first_call_resolve_may['vru_time']+first_call_resolve_may['q_time']).sum()/len(first_call_resolve_may)\n",
    "avg_response_time_jun=(first_call_resolve_jun['vru_time']+first_call_resolve_jun['q_time']).sum()/len(first_call_resolve_jun)\n",
    "\n",
    "#take a lot time in figuring out this problem\n",
    "avg_response_time_jul=(first_call_resolve_jul['vru_time']+first_call_resolve_jul['q_time']).sum()/len(first_call_resolve_jul)\n",
    "\n",
    "avg_response_time_aug=(first_call_resolve_aug['vru_time']+first_call_resolve_aug['q_time']).sum()/len(first_call_resolve_aug)\n",
    "avg_response_time_sep=(first_call_resolve_sep['vru_time']+first_call_resolve_sep['q_time']).sum()/len(first_call_resolve_sep)\n",
    "avg_response_time_oct=(first_call_resolve_oct['vru_time']+first_call_resolve_oct['q_time']).sum()/len(first_call_resolve_oct)\n",
    "avg_response_time_nov=(first_call_resolve_nov['vru_time']+first_call_resolve_nov['q_time']).sum()/len(first_call_resolve_nov)\n",
    "avg_response_time_dec=(first_call_resolve_dec['vru_time']+first_call_resolve_dec['q_time']).sum()/len(first_call_resolve_dec)\n",
    "\n",
    "#avg_response_time=[avg_response_time_jan/len(first_call_resolve_jan),avg_response_time_feb/len(first_call_resolve_feb),avg_response_time_mar/len(first_call_resolve_mar),avg_response_time_apr/len(first_call_resolve_apr),avg_response_time_may/len(first_call_resolve_may),avg_response_time_jun/len(first_call_resolve_jun),avg_response_time_jul/len(first_call_resolve_jul),avg_response_time_aug/len(first_call_resolve_aug),avg_response_sep/len(first_call_resolve_sep),avg_response_time_oct/len(first_call_resolve_oct),avg_response_time_nov/len(first_call_resolve_nov),avg_response_time_dec/len(first_call_resolve_dec)]\n",
    "avg_response_time_month=[avg_response_time_jan,avg_response_time_feb,avg_response_time_mar,avg_response_time_apr,avg_response_time_may,avg_response_time_jun,avg_response_time_jul,\n",
    "                  avg_response_time_aug,avg_response_time_sep,avg_response_time_oct,avg_response_time_nov,avg_response_time_dec]\n",
    "avg_response_time=pd.DataFrame(avg_response_time_month)\n",
    "\n",
    "\n",
    "# avg_response_time\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 7\n",
    "fig_size[1] = 5\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size\n",
    "\n",
    "plt.scatter(month,avg_response_time, s=500, c='purple')\n",
    "plt.ylabel(\"In Seconds\",fontsize=30)\n",
    "plt.title(\"Average Response Time Each Month\",fontsize=20)\n",
    "plt.xlabel(\"Time Taken To Service a Customer\",fontsize=20)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performance of Agent Month wise\n",
    "# number of call handled by each agent in month\n",
    "\n",
    "def count_agent_calls(agent_month,month):\n",
    "    count_agent={}\n",
    "    for i in eval('agent_'+month.lower())['server']:\n",
    "        count_agent[i]=count_agent.get(i,0)+1\n",
    "    return count_agent\n",
    "    \n",
    "# print(count_agent_calls(agent_jan,'jan')) \n",
    "\n",
    "def max_call_handle(agent_month,month):\n",
    "    \n",
    "    count_agent={}\n",
    "    for i in eval('agent_'+month.lower())['server']:\n",
    "        count_agent[i]=count_agent.get(i,0)+1\n",
    "    \n",
    "    \n",
    "    max_call_handled=max(count_agent.values())\n",
    "    \n",
    "    lst_of_names=dict()\n",
    "    for key, value in count_agent.items():\n",
    "        if max_call_handled==value:\n",
    "            lst_of_names.update({key:value})\n",
    "    return(lst_of_names)\n",
    "\n",
    "# print(max_call_handle(agent_oct,'oct'))\n",
    "\n",
    "# print(count_agent_calls(agent_feb,'feb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# call handled by each agent in hours\n",
    "\n",
    "def agent_call(agent_month):\n",
    "    agent_call_handling_hr=dict()\n",
    "    for index,row in agent_month.iterrows():\n",
    "        agent_call_handling_hr[row['server']]=agent_call_handling_hr.get(row['server'],0)+(row['ser_time']/3600)\n",
    "    return agent_call_handling_hr\n",
    "\n",
    "month_call=[agent_call(agent_jan),agent_call(agent_feb),agent_call(agent_mar),agent_call(agent_apr),\n",
    "                   agent_call(agent_may),agent_call(agent_jun),agent_call(agent_jul),agent_call(agent_aug),\n",
    "                   agent_call(agent_sep),agent_call(agent_oct),agent_call(agent_nov),agent_call(agent_dec)]\n",
    "\n",
    "sum_year_call=0\n",
    "\n",
    "for i in month_call:\n",
    "    for j,k in i.items():\n",
    "        sum_year_call=sum_year_call+k\n",
    "# print(sum_year_call)\n",
    "\n",
    "# call_this_year=sum(sum_of_month_call.values())\n",
    "\n",
    "def max_call_hour(agent_month):\n",
    "    agent_call_hour=dict()\n",
    "    for index,row in agent_month.iterrows():\n",
    "        agent_call_hour[row['server']]=agent_call_hour.get(row['server'],0)+(row['ser_time']/3600)\n",
    "    \n",
    "    \n",
    "    max_call_hour=max(agent_call_hour.values())\n",
    "    \n",
    "    lst_of_names=dict()\n",
    "    for key, value in agent_call_hour.items():\n",
    "        if max_call_hour==value:\n",
    "            lst_of_names.update({key:value})\n",
    "    return(lst_of_names)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------+-------+---------------+---------------------+\n",
      "|  Agent Name  | Month | Handled Calls | Total On Calls Hour |\n",
      "+--------------+-------+---------------+---------------------+\n",
      "|    SHARON    |  MAY  |      1423     |        59.03        |\n",
      "|    STEREN    |  MAY  |      1369     |        72.02        |\n",
      "|     ROTH     |  MAY  |      1157     |        84.40        |\n",
      "|    KAZAV     |  MAY  |      1138     |        75.78        |\n",
      "|    MORIAH    |  MAY  |      1104     |        64.17        |\n",
      "|     AVNI     |  MAY  |      1093     |        86.98        |\n",
      "|     AVI      |  MAY  |      1062     |        43.84        |\n",
      "|     TOVA     |  MAY  |      978      |        52.39        |\n",
      "|    YIFAT     |  MAY  |      947      |        51.45        |\n",
      "|     YITZ     |  MAY  |      857      |        51.63        |\n",
      "|    ZOHARI    |  MAY  |      822      |        67.97        |\n",
      "|     MIKI     |  MAY  |      707      |        41.15        |\n",
      "|     GILI     |  MAY  |      665      |        31.74        |\n",
      "|    VICKY     |  MAY  |      569      |        36.93        |\n",
      "|    BASCH     |  MAY  |      488      |        25.54        |\n",
      "|    GELBER    |  MAY  |      462      |        19.94        |\n",
      "|    DARMON    |  MAY  |      440      |        36.29        |\n",
      "|    AVIDAN    |  MAY  |      433      |        30.40        |\n",
      "|   BENSION    |  MAY  |      415      |        21.85        |\n",
      "|     ELI      |  MAY  |      304      |        41.32        |\n",
      "|    DORIT     |  MAY  |      285      |        17.20        |\n",
      "| NO_SERVERAMA |  MAY  |      239      |        12.47        |\n",
      "|    SHLOMO    |  MAY  |       33      |         1.43        |\n",
      "|    PINHAS    |  MAY  |       21      |         2.59        |\n",
      "|    DAVID     |  MAY  |       2       |         0.00        |\n",
      "+--------------+-------+---------------+---------------------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable\n",
    "\n",
    "\n",
    "def table_for_month(agent_month,month):\n",
    "    x = PrettyTable()\n",
    "    month=month.lower()\n",
    "    x.field_names = [\"Agent Name\",\"Month\",\"Handled Calls\", \"Total On Calls Hour\"]\n",
    "    agent_call_handling_hr=dict()\n",
    "    for index,row in agent_month.iterrows():\n",
    "        agent_call_handling_hr[row['server']]=agent_call_handling_hr.get(row['server'],0)+(row['ser_time']/3600)\n",
    "#     total_hr_of_month=0\n",
    "    count_agent={}\n",
    "    count_agent=count_agent_calls(agent_month,month)\n",
    "    for i,j in agent_call_handling_hr.items():\n",
    "        x.add_row([i,month.upper(),count_agent.get(i,0), '%.2f' %j])\n",
    "#         total_hr_of_month=total_hr_of_month+j\n",
    "    x.sortby=\"Handled Calls\"\n",
    "    x.reversesort=True\n",
    "#     writing table to file\n",
    "#     data=x.get_string()\n",
    "#     with open('text.txt', 'w') as f:\n",
    "#         f.write(data)\n",
    "        \n",
    "#  end of writing table to file\n",
    "    \n",
    "    return (x)\n",
    "\n",
    "print(table_for_month(agent_may,'may'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# top performer of month\n",
    "\n",
    "# print(max_call_hour(agent_jan))\n",
    "# print(max_call_hour(agent_feb))\n",
    "# print(max_call_hour(agent_mar))\n",
    "# print(max_call_hour(agent_apr))\n",
    "# print(max_call_hour(agent_may))\n",
    "# print(max_call_hour(agent_jun))\n",
    "# print(max_call_hour(agent_jul))\n",
    "# print(max_call_hour(agent_aug))\n",
    "# print(max_call_hour(agent_sep))\n",
    "# print(max_call_hour(agent_oct))\n",
    "# print(max_call_hour(agent_nov))\n",
    "# print(max_call_hour(agent_dec))\n",
    "\n",
    "# top performer of Year\n",
    "performer_of_year=[max_call_hour(agent_jan),max_call_hour(agent_feb),max_call_hour(agent_mar),max_call_hour(agent_apr),\n",
    "                  max_call_hour(agent_may),max_call_hour(agent_jun),max_call_hour(agent_jul),max_call_hour(agent_aug),\n",
    "                  max_call_hour(agent_sep),max_call_hour(agent_oct),max_call_hour(agent_nov),max_call_hour(agent_dec)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AVNI\n"
     ]
    }
   ],
   "source": [
    "# top performing agent of the year \n",
    "top_performer_of_year={}\n",
    "for i in performer_of_year:\n",
    "    for j in i:\n",
    "        top_performer_of_year[j]=top_performer_of_year.get(j,0)+1\n",
    "        \n",
    "# print(top_performer_of_year)\n",
    "performer=sorted(top_performer_of_year.items(),key=lambda x:x[1],reverse=True)\n",
    "print(performer[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import tkinter as tk\n",
    "\n",
    "from tkinter import ttk\n",
    "from tkinter import *\n",
    "from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n",
    "from matplotlib.figure import Figure\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import DataFrame\n",
    "import getpass\n",
    "from datetime import datetime\n",
    "#print(getpass.getuser())\n",
    "root=Tk()\n",
    "#scrollbar\n",
    "\n",
    "# /scrollbar\n",
    "root.title('Retail Analytics')\n",
    "root.configure(bg='#ffffff')\n",
    "#root.attributes('-fullscreen',True)\n",
    "s_width = root.winfo_screenwidth()\n",
    "s_height = root.winfo_screenheight()\n",
    "root.geometry(\"%dx%d\" % (s_width, s_height))\n",
    "#root.geometry('1280x720+110+0')\n",
    "root.state('zoomed')\n",
    "\n",
    "\n",
    "\n",
    " \n",
    "\n",
    "\n",
    "upr_fr=Frame(root,cursor='arrow',relief=RIDGE,highlightthickness=2,bg='white',bd=2,width=s_width,height=120)\n",
    "upr_fr.place(x=10,y=-10)\n",
    "\n",
    "fr1=Frame(root,cursor='arrow',relief=RIDGE,bg='white',bd=2,width=230,height=120)\n",
    "fr1.place(x=290,y=130)\n",
    "\n",
    "fr2=Frame(root,cursor='arrow',relief=RIDGE,highlightthickness=2,bg='white',bd=2,width=230,height=120)\n",
    "fr2.place(x=550,y=130)\n",
    "\n",
    "fr3=Frame(root,cursor='arrow',relief=RIDGE,highlightthickness=2,bg='white',bd=2,width=230,height=120)\n",
    "fr3.place(x=800,y=130)\n",
    "\n",
    "fr4=Frame(root,cursor='arrow',relief=RIDGE,highlightthickness=2,bg='white',bd=2,width=230,height=120)\n",
    "fr4.place(x=1050,y=130)\n",
    "\n",
    "fr5=Frame(root,cursor='arrow',relief=RIDGE,highlightthickness=2,bg='white',bd=2,width=500,height=350)\n",
    "fr5.place(x=290,y=270)\n",
    "\n",
    "fr6=Frame(root,cursor='arrow',relief=RIDGE,highlightthickness=2,bg='white',bd=2,width=500,height=350)\n",
    "fr6.place(x=820,y=270)\n",
    "\n",
    "side_frame=Frame(root,cursor='arrow',relief=RIDGE,highlightthickness=2,bg='#2c343c',bd=2,width=270,height=1080)\n",
    "side_frame.place(x=-10,y=-100)\n",
    "\n",
    "Center_side_frame=Frame(root,cursor='arrow',relief=RIDGE,highlightthickness=2,bg='#2c343c',bd=2,width=270,height=1080)\n",
    "side_frame.place(x=-20,y=-100)\n",
    "\n",
    "\n",
    "fr1_Lbl_1=Label(fr1,relief=FLAT,bd=0,bg='white',fg='BLUE',font=(\"Segoe UI\",13),highlightthickness=0,text='Total Call Hour This Year ')\n",
    "fr1_Lbl_1.place(x=25,y=8)\n",
    "\n",
    "\n",
    "fr1_Lbl_1=Label(fr1,relief=FLAT,bd=0,bg='white',fg='BROWN',font=(\"Segoe UI\",16),highlightthickness=0,text=str('%.2f' %(sum_year_call))+'  Hour')\n",
    "fr1_Lbl_1.place(x=40,y=40)\n",
    "\n",
    "#fr1_Lbl_1.configure(anchor='center')\n",
    "fr2_Lbl_1=Label(fr2,relief=FLAT,bd=0,bg='white',fg='BLUE',font=(\"Segoe UI\",13),highlightthickness=0,text='Performer of the Year\\n')\n",
    "fr2_Lbl_1.place(x=15,y=2)\n",
    "\n",
    "# fr2_Lbl_1['state']=DISABLED\n",
    "\n",
    "fr2_Lbl_1=Label(fr2,relief=FLAT,bd=0,bg='white',fg='BROWN',font=(\"Segoe UI\",16),highlightthickness=3,text=performer[0][0]+'\\n'+str(performer[0][1])+'  Times in the Year'+' ')\n",
    "fr2_Lbl_1.place(x=10,y=40)\n",
    "\n",
    "\n",
    "fr3_Lbl_1=Label(fr3,relief=FLAT,bd=0,bg='white',fg='BLUE',font=(\"Segoe UI\",12),highlightthickness=0,text='Average Response Time(Year)')\n",
    "fr3_Lbl_1.place(x=10,y=2)\n",
    "\n",
    "fr2_Lbl_1=Label(fr3,relief=FLAT,bd=0,bg='white',fg='BROWN',font=(\"Segoe UI\",16),highlightthickness=3,text=str('%.2f' %(sum(avg_response_time_month)/12))+'  Seconds')\n",
    "fr2_Lbl_1.place(x=40,y=40)\n",
    "\n",
    "fr4_Lbl_1=Label(fr4,relief=FLAT,bd=0,bg='white',fg='BLUE',font=(\"Segoe UI\",12),highlightthickness=0,text='First Call Response\\nThis Month')\n",
    "fr4_Lbl_1.place(x=40,y=2)\n",
    "\n",
    "fr4_Lbl_1=Label(fr4,relief=FLAT,bd=0,bg='white',fg='BROWN',font=(\"Segoe UI\",16),highlightthickness=0,text=str('%.2f' %(avg_response_time_month[datetime.now().month-1]))+'  Seconds')\n",
    "fr4_Lbl_1.place(x=40,y=43)\n",
    "\n",
    "\n",
    "\n",
    "#upr Frame codes\n",
    "fr1_Lbl_1=Label(upr_fr,relief=FLAT,bd=0,bg='white',fg='GREEN',font=(\"Segoe UI\",13),highlightthickness=0,text='Hello, %s'%(getpass.getuser()))\n",
    "fr1_Lbl_1.place(x=s_width-300,y=20)\n",
    "\n",
    "fr1_Lbl_2=Label(upr_fr,cursor='hand2',relief=RIDGE,bd=0,bg='white',fg='Red',font=(\"Segoe UI\",13),highlightthickness=0,text='Logout')\n",
    "fr1_Lbl_2.place(x=s_width-150,y=20)\n",
    "\n",
    "fr1_Lbl_3=Label(upr_fr,relief=FLAT,bd=0,bg='white',fg='BLACK',font=(\"Segoe UI\",30,'bold'),highlightthickness=0,text='Dashboard of Phone Quality')\n",
    "fr1_Lbl_3.place(x=350,y=50)\n",
    "\n",
    "# fr1_Lbl_4=Label(upr_fr,relief=FLAT,bd=0,bg='white',fg='BLACK',font=(\"Segoe UI\",13,'italic'),highlightthickness=0,text='MCA Intern')\n",
    "# fr1_Lbl_4.place(x=550,y=62)\n",
    "\n",
    "# fr1_Lbl_4=Label(upr_fr,relief=FLAT,bd=0,bg='white',fg='BLACK',font=(\"Segoe UI\",13),highlightthickness=0,text='AnupGaurav@exlservice.com')\n",
    "# fr1_Lbl_4.place(x=350,y=85)\n",
    "\n",
    "\n",
    "\n",
    "data1={'Month':['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'],\n",
    "           'Volume':[len(df_jan.index),len(df_feb.index),len(df_mar.index),len(df_apr.index),\n",
    "                         len(df_may.index),len(df_jun.index),len(df_jul.index),len(df_aug.index),\n",
    "                         len(df_sep.index),len(df_oct.index),len(df_nov.index),len(df_dec.index)]}\n",
    "    \n",
    "    \n",
    "    \n",
    "figure1 = plt.Figure(figsize=(5,4.36), dpi=92)\n",
    "ax1 = figure1.add_subplot(111)\n",
    "bar1 = FigureCanvasTkAgg(figure1,fr5)\n",
    "bar1.get_tk_widget().pack(side=tk.BOTTOM, fill=tk.BOTH)\n",
    "\n",
    "df1 = pd.DataFrame(data1,columns = ['Month','Volume'])\n",
    "df1 = df1[['Month','Volume']].groupby('Month').sum()\n",
    "df1.plot(kind='bar', legend=True, ax=ax1)\n",
    "ax1.axhline(y=8,linewidth=2,color='g')\n",
    "ax1.set_xticklabels(df1.index,rotation=20)\n",
    "ax1.set_title('Volume Of Data Generated Each Month')\n",
    "        \n",
    "fig = plt.Figure(figsize=(5,4.36),dpi=90)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "colors = ['#99ff99','#ff9999','#ff6161','#b0ff61','#61d7ff','#6161ff','#EFC050','#98B4D4','#E15D44']\n",
    "ax.pie(data1['Volume'],labels=data1['Month'], explode=(0.1, 0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0), colors=colors, autopct='%1.1f%%',shadow=True, startangle=270)\n",
    "\n",
    "ax.set_xticklabels(df1.index,rotation=20)\n",
    "ax.set_title(\"% of Calls Every Month In a Year \")\n",
    "canvas = FigureCanvasTkAgg(fig, fr6)\n",
    "canvas.get_tk_widget().pack()\n",
    "canvas.draw()\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "def create_charts():\n",
    "    \n",
    "# destroying frames\n",
    "    fr1.destroy()\n",
    "    fr2.destroy()\n",
    "    fr3.destroy()\n",
    "    fr4.destroy()\n",
    "    fr5.destroy()\n",
    "    fr6.destroy()\n",
    "    \n",
    "    \n",
    "\n",
    "    def table():\n",
    "        Month=['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']\n",
    "        agent_call_handling_hr=dict()\n",
    "        call=dict()\n",
    "        for index,row in eval('agent_'+str(Month[datetime.now().month-1])).iterrows():\n",
    "            agent_call_handling_hr[row['server']]=agent_call_handling_hr.get(row['server'],0)+(row['ser_time']/3600)\n",
    "#             call[row['server']]=call.get[row['server'],0]+1\n",
    "\n",
    "        frame = Frame(root,cursor='arrow',relief=RIDGE,bg='white',bd=2,width=550,height=550)\n",
    "        frame.pack()\n",
    "        frame.place(x=290,y=150)\n",
    "        \n",
    "        \n",
    "        frame1 = Frame(root,cursor='arrow',relief=RIDGE,bg='white',bd=2,width=150,height=25)\n",
    "        frame1.pack()\n",
    "        frame1.place(x=350,y=100)\n",
    "        frame_1=Label(frame1,relief=FLAT,bd=0,bg='white',fg='BLUE',font=(\"Segoe UI\",10),highlightthickness=0,text=str((Month[datetime.now().month-1]).upper()))\n",
    "        frame_1.place(x=50,y=-0)\n",
    "#         fr1_Lbl_1.place(x=25,y=8)\n",
    "\n",
    "\n",
    "        tree = ttk.Treeview(frame, columns = (1,2,3), height = 25, show = \"headings\")\n",
    "        tree.pack(side = 'left')\n",
    "\n",
    "        tree.heading(1, text=\"Agent name\")\n",
    "        tree.heading(2, text=\"Month\")\n",
    "#         tree.heading(3, text=\"Call handled\")\n",
    "        tree.heading(3, text=\"Hours on Call\")\n",
    "        \n",
    "#         tree.heading(3, text=\"Hours on Call\")\n",
    "\n",
    "        tree.column(1, width = 100)\n",
    "        tree.column(2, width = 150)\n",
    "        tree.column(3, width = 150)\n",
    "#         tree.column(3, width = 100)\n",
    "\n",
    "        scroll = ttk.Scrollbar(frame, orient=\"vertical\", command=tree.yview)\n",
    "        scroll.pack(side = 'right', fill = 'y')\n",
    "\n",
    "        tree.configure(yscrollcommand=scroll.set)\n",
    "\n",
    "        for i,j in agent_call_handling_hr.items():\n",
    "            tree.insert('', 'end', values = (i,Month[datetime.now().month-1],'%.2f'%(j)) )\n",
    "            \n",
    "    table()   \n",
    "            \n",
    "    \n",
    "#         frame2 = Frame(root,cursor='arrow',relief=RIDGE,bg='white',bd=2,width=400,height=100)\n",
    "#         frame2.pack()\n",
    "#         frame2.place(x=630,y=150)\n",
    "# #         top  frame\n",
    "#         frame_2 = Frame(root,cursor='arrow',relief=RIDGE,bg='white',bd=2,width=150,height=25)\n",
    "#         frame_2.pack()\n",
    "#         frame_2.place(x=750,y=100)\n",
    "        \n",
    "#         frame_2=Label(frame_2,relief=FLAT,bd=0,bg='white',fg='BLUE',font=(\"Segoe UI\",10),highlightthickness=0,text=str((Month[datetime.now().month-2]).upper()))\n",
    "#         frame_2.place(x=50,y=-1)\n",
    "        \n",
    "        \n",
    "\n",
    "#         tree = ttk.Treeview(frame, columns = (1,2), height = 25, show = \"headings\")\n",
    "#         tree.pack(side = 'left')\n",
    "\n",
    "#         tree.heading(1, text=\"Agent name\")\n",
    "#         tree.heading(2, text=\"Hours on Call\")\n",
    "# #         tree.heading(3, text=\"Hours on Call\")\n",
    "\n",
    "#         tree.column(1, width = 100)\n",
    "#         tree.column(2, width = 150)\n",
    "# #         tree.column(3, width = 100)\n",
    "\n",
    "#         scroll = ttk.Scrollbar(frame, orient=\"vertical\", command=tree.yview)\n",
    "#         scroll.pack(side = 'right', fill = 'y')\n",
    "\n",
    "#         tree.configure(yscrollcommand=scroll.set)\n",
    "\n",
    "#         for i,j in agent_call_handling_hr.items():\n",
    "#             tree.insert('', 'end', values = (i,'%.2f'%(j)) )\n",
    "      \n",
    "\n",
    "\n",
    "            \n",
    "    \n",
    "# #################################################################### \n",
    "    \n",
    "#     fig = plt.Figure(figsize=(5,4.36),dpi=90)\n",
    "#     ax = fig.add_subplot(111)\n",
    "\n",
    "#     colors = ['#99ff99','#ff9999','#ff6161','#b0ff61','#61d7ff','#6161ff','#EFC050','#98B4D4','#E15D44']\n",
    "#     ax.pie(data1['Volume'],labels=data1['Month'], explode=(0.1, 0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0), colors=colors, autopct='%1.1f%%',shadow=True, startangle=270)\n",
    "\n",
    "#     ax.set_xticklabels(df1.index,rotation=20)\n",
    "#     ax.set_title(\"% of Calls Every Month In a Year \")\n",
    "#     canvas = FigureCanvasTkAgg(fig, fr6)\n",
    "#     canvas.get_tk_widget().pack()\n",
    "#     canvas.draw()\n",
    "\n",
    "###################################################################  \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "#end of define create_charts\n",
    "\n",
    "\n",
    "\n",
    "#side frAME CODES\n",
    "fr1_L=Label(side_frame,text=' Retail \\n Analytics',justify='center',fg='#40E0D0',bg='#2c343c',relief=FLAT,\n",
    "            height=2,width=13,font=(\"Arial\",23,'bold'))\n",
    "fr1_L.place(x=5,y=120)\n",
    "\n",
    "sfr_Btn_1=Button(side_frame,cursor='hand2',command='''create_charts''',relief=FLAT,bg='white',height=1,width=23,text='Main Dashboard',font=(\"Segoe UI\",14)) \n",
    "sfr_Btn_1.place(x=5,y=350)\n",
    "\n",
    "sfr_Btn_1=Button(side_frame,cursor='hand2',command=create_charts,relief=FLAT,bg='white',height=1,width=23,text='Months Dashboard',font=(\"Segoe UI\",14))\n",
    "sfr_Btn_1.place(x=1,y=395)\n",
    "\n",
    "sfr_Btn_1=Button(side_frame,cursor='hand2',relief=FLAT,bg='white',height=1,width=23,text='Send Request',font=(\"Segoe UI\",14))\n",
    "sfr_Btn_1.place(x=1,y=440)\n",
    "\n",
    "\n",
    "  \n",
    "# Resizing image to fit on button \n",
    "#photoimage = photo.subsample(3, 3) \n",
    "Month=['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']\n",
    "agent_call_handling_hr=dict()\n",
    "for index,row in eval('agent_'+str(Month[datetime.now().month-1])).iterrows():\n",
    "    agent_call_handling_hr[row['server']]=agent_call_handling_hr.get(row['server'],0)+(row['ser_time']/3600)\n",
    "#sfr_Btn_1=Button(side_frame,cursor='hand2',relief=FLAT,bg='white',height=1,width=23, image = photoimage,compound = LEFT,text='Send Request',font=(\"Segoe UI\",14))\n",
    "#sfr_Btn_1.place(x=1,y=390)\n",
    "\n",
    "root.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "        "
   ]
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